diff --git a/README.md b/README.md
index dc5d448..71e3607 100644
--- a/README.md
+++ b/README.md
@@ -29,11 +29,11 @@ The library provides `QuantizedTensor`, a `torch.Tensor` subclass that transpare
 | `TensorCoreMXFP8Layout`| MXFP8 E4M3   | SM ≥ 10.0 (Blackwell) | Block quantization with 32-element blocks, E8M0 scales |
 
 ```python
-from comfy_kitchen.tensor import QuantizedTensor, TensorCoreFP8Layout, TensorCoreNVFP4Layout
+from comfy_kitchen.tensor import QuantizedTensor
 
 # Quantize a tensor
 x = torch.randn(128, 256, device="cuda", dtype=torch.bfloat16)
-qt = QuantizedTensor.from_float(x, TensorCoreFP8Layout)
+qt = QuantizedTensor.from_float(x, "TensorCoreFP8Layout")
 
 # Operations dispatch to optimized kernels automatically
 output = torch.nn.functional.linear(qt, weight_qt)
diff --git a/comfy_kitchen/tensor/fp8.py b/comfy_kitchen/tensor/fp8.py
index 2c20bc1..d53e376 100644
--- a/comfy_kitchen/tensor/fp8.py
+++ b/comfy_kitchen/tensor/fp8.py
@@ -23,7 +23,7 @@ class TensorCoreFP8Layout(QuantizedLayout):
 
     Example:
         >>> x = torch.randn(128, 256, device="cuda", dtype=torch.bfloat16)
-        >>> qt = QuantizedTensor.from_float(x, TensorCoreFP8Layout)
+        >>> qt = QuantizedTensor.from_float(x, "TensorCoreFP8Layout")
         >>> qt.shape
         torch.Size([128, 256])
         >>> dq = qt.dequantize()